23.Many-qubit Hamiltonians

preview_player
Показать описание
  1. Jochen Rau
Рекомендации по теме
23.Many-qubit Hamiltonians 0:20:06

23.Many-qubit Hamiltonians

Yu Tong: Learning 0:50:39

Yu Tong: Learning Many-body Hamiltonians with Heisenberg-limited Scaling | QHack 2023

23. Quantum Chemistry 1:00:36

23. Quantum Chemistry I: Obtaining the Qubit Hamiltonian for H2 and LiH - Part 2

NLTS Hamiltonians from 1:04:45

NLTS Hamiltonians from Codes | Quantum Colloquium

The Transmon Qubit 0:25:31

The Transmon Qubit

Evolution of Hamiltonians 0:09:09

Evolution of Hamiltonians formed by products of Pauli operators: quantum circuits & exact simula...

Quantum Algorithm for 0:41:07

Quantum Algorithm for Simulating Real Time Evolution of Lattice Hamiltonians

22. Quantum Chemistry 0:50:13

22. Quantum Chemistry I: Obtaining the Qubit Hamiltonian for H2 and LiH - Part 1

The Area-Law Conjecture 1:23:26

The Area-Law Conjecture in Many-Body Quantum Physics | Quantum Colloquium

Quantum Computing With 0:59:23

Quantum Computing With Stabilized Cat Qubits

David Gosset | 0:48:34

David Gosset | Approximation algorithms for quantum many-body problems

Spin Qubits by 1:31:25

Spin Qubits by Guido Burkard

2QAN: A quantum 0:30:48

2QAN: A quantum compiler for 2-local qubit Hamiltonian simulation algorithms - Dan E. Browne

QIP2021 | Efficient 0:28:43

QIP2021 | Efficient quantum computation of chemistry through tensor hypercontraction (Dominic Berry)

Variational Quantum Eigensolver 0:27:29

Variational Quantum Eigensolver (VQE) for 2 Qubit Hamiltonians in TensorFlow-Quantum (TFQ)

Di Fang - 0:35:16

Di Fang - Time-dependent Hamiltonian Simulation of Highly Oscillatory Dynamics - IPAM at UCLA

Universal control using 1:16:57

Universal control using the quantum Zeno effect | Seminar Series with Shay Hacohen-Gourgy

Approximation Algorithms for 0:24:03

Approximation Algorithms for Bounded-Degree Local Hamiltonians

Machine Learning for 0:14:28

Machine Learning for Quantum Simulation - Robert Huang (June 23, 2020)

Семинар 2: Quantum 1:00:38

Семинар 2: Quantum dynamics of time dependent Hamiltonian models: from single qubit to interacting q...

Rafael Nepomechie: Bethe 1:20:52

Rafael Nepomechie: Bethe ansatz and quantum computing

QIP2021 | Exploiting 0:29:58

QIP2021 | Exploiting Contextuality in Variational Quantum Eigensolvers (William Kirby)

The open quantum 0:39:53

The open quantum system toolkit for Hamiltonian quantum computing

QIP2023 | Learning 0:25:31

QIP2023 | Learning to predict arbitrary quantum processes (Hsin-Yuan Huang)

Комментарии
Автор

Professor, the Hamiltonian of an electron can be mapped to the Hamiltonian of a qubit by the Jordan method. We have the formula you gave, and the multi-body Hamiltonian can be expressed in the form of coefficient multiplied by Pauli basis. But is this linear combination unique? The so-called optimization, is it necessary to find the smallest sum of coefficients of a linear combination? In addition, I can understand what you said, the tensor product of the identity matrix I and I does not need to be measured. Z and I, and Z and Z are measured directly, but I still can't understand the situation of X and X after listening to your speech. So if X and I appear (does it need to be measured after adding H GATE), how should we solve the tensor product of Y and Y? What about the tensor product of Y and X?

rx